In [1]:
import os

## Set directory
os.chdir('/hpc/group/pbenfeylab/CheWei/CW_data/genesys')

import networkx as nx
from genesys_evaluate_v1 import *
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import warnings
# Suppress all warning messages
warnings.filterwarnings("ignore", category=DeprecationWarning)

/hpc/group/pbenfeylab/ch416/miniconda3/envs/genesys/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm
In [2]:
## Conda Env pytorch-gpu on DCC
print(torch.__version__)
print(sc.__version__) 

1.11.0
1.9.6
In [3]:
## Genes considered/used (shared among samples) 
gene_list = pd.read_csv('./gene_list_1108.csv')

Load Data¶

In [4]:
with open("./genesys_root_data.pkl", 'rb') as file_handle:
    data = pickle.load(file_handle)
    
batch_size = 2000
dataset = Root_Dataset(data['X_test'], data['y_test'])
loader = DataLoader(dataset,
                         batch_size = batch_size,
                         shuffle = True, drop_last=True)
In [5]:
input_size = data['X_train'].shape[1]
## 10 cell types 
output_size = 10
embedding_dim = 256
hidden_dim = 256
n_layers = 2
device = "cpu"
path = "./"

Load trained GeneSys model¶

In [6]:
model = ClassifierLSTM(input_size, output_size, embedding_dim, hidden_dim, n_layers).to(device)
model.load_state_dict(torch.load(path+"/workstation/genesys_model_trained_on_root_atlas_20240308_continue4.pth", map_location=torch.device('cpu')))
model = model
model.eval()
Out[6]:
ClassifierLSTM(
  (fc1): Sequential(
    (0): Linear(in_features=17513, out_features=256, bias=True)
    (1): Dropout(p=0.2, inplace=False)
    (2): GaussianNoise()
  )
  (fc): Sequential(
    (0): ReLU()
    (1): Linear(in_features=512, out_features=512, bias=True)
    (2): ReLU()
    (3): Linear(in_features=512, out_features=10, bias=True)
  )
  (lstm): LSTM(256, 256, num_layers=2, batch_first=True, dropout=0.2, bidirectional=True)
  (dropout): Dropout(p=0.2, inplace=False)
  (b_to_z): DBlock(
    (fc1): Linear(in_features=512, out_features=256, bias=True)
    (fc2): Linear(in_features=512, out_features=256, bias=True)
    (fc_mu): Linear(in_features=256, out_features=512, bias=True)
    (fc_logsigma): Linear(in_features=256, out_features=512, bias=True)
  )
  (bz2_infer_z1): DBlock(
    (fc1): Linear(in_features=1024, out_features=256, bias=True)
    (fc2): Linear(in_features=1024, out_features=256, bias=True)
    (fc_mu): Linear(in_features=256, out_features=512, bias=True)
    (fc_logsigma): Linear(in_features=256, out_features=512, bias=True)
  )
  (z1_to_z2): DBlock(
    (fc1): Linear(in_features=512, out_features=256, bias=True)
    (fc2): Linear(in_features=512, out_features=256, bias=True)
    (fc_mu): Linear(in_features=256, out_features=512, bias=True)
    (fc_logsigma): Linear(in_features=256, out_features=512, bias=True)
  )
  (z_to_x): Decoder(
    (fc1): Linear(in_features=512, out_features=256, bias=True)
    (fc2): Linear(in_features=256, out_features=256, bias=True)
    (fc3): Linear(in_features=256, out_features=17513, bias=True)
  )
)
In [7]:
classes = ['Columella', 'Lateral Root Cap', 'Phloem', 'Xylem', 'Procambium', 'Pericycle', 'Endodermis', 'Cortex', 'Atrichoblast', 'Trichoblast']
class2num = {c: i for (i, c) in enumerate(classes)}
num2class = {i: c for (i, c) in enumerate(classes)}
In [8]:
cts = ['Atrichoblast','Trichoblast','Cortex','Endodermis','Pericycle','Procambium','Xylem','Phloem','Lateral Root Cap','Columella']
ctw = np.zeros((len(cts), 17513, 17513))
## number of cells sampled from the atlas
batch_size = 2000
In [9]:
## GRN for the transition t0 to t1
for ct in cts:
    print(ct)
    cws = np.zeros((len(loader), 17513, 17513))
    with torch.no_grad():
        for i, sample in enumerate(loader):
            x = sample['x'].to(device)
            y = sample['y'].to(device)
            y_label = [num2class[i] for i in y.tolist()]
            
            pred_h = model.init_hidden(batch_size)
            tfrom = model.generate_current(x, pred_h, 0).to('cpu').detach().numpy()
            cfrom = tfrom[np.where(np.array(y_label)==ct)[0],:]
            
            pred_h = model.init_hidden(batch_size)
            tto = model.generate_next(x, pred_h, 0).to('cpu').detach().numpy()   
            cto = tto[np.where(np.array(y_label)==ct)[0],:]
            
            cw = torch.linalg.lstsq(torch.tensor(cfrom), torch.tensor(cto)).solution.detach().numpy()
            cws[i] = cw
    
    ## Calculate mean across number of repeats
    cwm = np.mean(cws, axis=0)
    ctw[cts.index(ct)] = cwm

Atrichoblast
Trichoblast
Cortex
Endodermis
Pericycle
Procambium
Xylem
Phloem
Lateral Root Cap
Columella
In [10]:
# Save the array to disk
np.save('genesys_ctw_t0-t1.npy', ctw)
In [11]:
ctw = np.load('genesys_ctw_t0-t1.npy')
In [12]:
## Calculate z-scores
ctw_z = np.zeros((len(cts), 17513, 17513))
for i in range(len(cts)):
    ctw_z[i] = (ctw[i] - np.mean(ctw[i])) / np.std(ctw[i])
In [13]:
## Filtering based on z-scores (with no weights)
ctw_f = np.zeros((len(cts), 17513, 17513))
## z-score threshold (keep values > mean + threshold*std)
threshold=3
for i in range(len(cts)):
    ctw_f[i] = np.abs(ctw_z[i]) > threshold

Load TFs list¶

In [14]:
wanted_TFs = pd.read_csv("./Kay_TF_thalemine_annotations.csv")
In [15]:
## Make TF names unique and assign preferred names
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G33880"]="WOX9"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G45160"]="SCL27"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G04410"]="NAC78"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G29035"]="ORS1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G02540"]="ZHD3"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G16500"]="IAA26"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G09740"]="HAG5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT4G24660"]="ZHD2"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G46880"]="HDG5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G28420"]="RLT1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G14580"]="BLJ"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G45260"]="BIB"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G02070"]="RVN"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G28160"]="FIT"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G68360"]="GIS3"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G20640"]="NLP4"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G05550"]="VFP5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G59470"]="FRF1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G15150"]="HAT7"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G14750"]="WER"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G75710"]="BRON"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G74500"]="TMO7"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G12646"]="RITF1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G48100"]="ARR5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT4G16141"]="GATA17L"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G65640"]="NFL"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G62700"]="VND5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT4G36160"]="VND2"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G66300"]="VND3"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G12260"]="VND4"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G62380"]="VND6"
In [16]:
pd.Series(wanted_TFs['Name']).value_counts().head(5)
Out[16]:
Name
NAC001    1
PRE5      1
MYB118    1
MYB21     1
MYB0      1
Name: count, dtype: int64

Network analysis¶

In [17]:
TFidx = []
for i in wanted_TFs['GeneID']:
    if i in gene_list['features'].tolist():
        TFidx.append(np.where(gene_list['features']==i)[0][0])

TFidx = np.sort(np.array(TFidx))
In [55]:
def network(i):
    ## No weights
    adj_nw = ctw_f[i]
    ## Weighted
    adj = ctw[i]*ctw_f[i]
    ## TF only
    adj = adj[np.ix_(TFidx,TFidx)]
    adj_nw = adj_nw[np.ix_(TFidx,TFidx)]
    
    ## Remove no connect 
    regidx = np.sort(np.array(pd.Series(np.where(adj_nw==True)[0]).value_counts().index[pd.Series(np.where(adj_nw==True)[0]).value_counts()>=1]))
    taridx = np.sort(np.array(pd.Series(np.where(adj_nw==True)[1]).value_counts().index[pd.Series(np.where(adj_nw==True)[1]).value_counts()>=1]))
    ## Reciprocol
    keepidx = np.sort(np.array(list(set(regidx).intersection(taridx))))
    #keepidx = np.sort(np.array(list(set(regidx).union(taridx))))
    
    TFID = np.array(gene_list['features'][TFidx])[keepidx].tolist()
    ## TF name to keep
    TFname = []
    for i in np.array(gene_list['features'][TFidx])[keepidx]:
        TFname.append(wanted_TFs['Name'][np.where(wanted_TFs['GeneID']==i)[0][0]])
        
    adj = adj[np.ix_(keepidx,keepidx)]
    
    # Create a NetworkX graph for non-directed edges
    G = nx.Graph()  # supports directed edges and allows for multiple edges between the same pair of nodes
    
    # Add nodes to the graph
    num_nodes = adj.shape[0]
    for i, name in enumerate(TFname):
        G.add_node(i, name=name)
    
    # Add edges to the graph with weights
    for i in range(num_nodes):
        for j in range(num_nodes):
            weight = adj[i, j]
            if weight != 0:
                G.add_edge(j, i, weight=abs(weight), distance=1/abs(weight))
            

    ## Measures the extent to which how close a node is to all other nodes in the network, considering the shortest paths or geodesic distances between nodes
    closeness_centrality = nx.closeness_centrality(G, distance='distance')
    ## Measures the extent to which a node that are not only well-connected but also connected to other well-connected nodes.
    eigenvector_centrality = nx.eigenvector_centrality(G)
    
    # Create a NetworkX graph for directed edges
    G = nx.MultiDiGraph()  # supports directed edges and allows for multiple edges between the same pair of nodes
    
    # Add nodes to the graph
    num_nodes = adj.shape[0]
    for i, name in enumerate(TFname):
        G.add_node(i, name=name)
    
    # Add edges to the graph with weights
    for i in range(num_nodes):
        for j in range(num_nodes):
            weight = adj[i, j]
            if weight != 0:
                G.add_edge(j, i, weight=weight)
    
    ## Measures the number of connections (edges) each node has
    degree_centrality = nx.degree_centrality(G)
    # Calculate outgoing centrality
    out_centrality = nx.out_degree_centrality(G)
    # Calculate incoming centrality
    in_centrality = nx.in_degree_centrality(G)
    ## Measures the extent to which a node lies on the shortest paths between other nodes.
    betweenness_centrality = nx.betweenness_centrality(G, weight='weight')
    
    ## Non_Reciprocal Out centrality
    # Visualize the graph
    pos = nx.spring_layout(G)  # Positions of the nodes
    
    # Node colors based on weighted betweenness centrality
    node_colors = [out_centrality[node] for node in G.nodes()]
    
    # Node sizes based on weighted betweenness centrality
    node_sizes = [out_centrality[node] * 1000 for node in G.nodes()]

    # Get the edge weights as a dictionary
    edge_weights = nx.get_edge_attributes(G, 'weight')
    edge_colors = ['red' if weight > 0 else 'blue' for (_, _, weight) in G.edges(data='weight')]
    
    # Scale the edge weights to desired linewidths
    max_weight = max(edge_weights.values())
    edge_widths = [float(edge_weights[edge]) / max_weight for edge in G.edges]
    
    # Draw the graph
    nx.draw(G, pos=pos, node_color=node_colors, node_size=node_sizes, with_labels=False, width=edge_widths, edge_color=edge_colors)
    # Add node labels
    labels = {node: G.nodes[node]['name'] for node in G.nodes}
    nx.draw_networkx_labels(G, pos=pos, labels=labels, font_size=8)
    
    # Add a colorbar to show the weighted betweenness centrality color mapping
    sm = plt.cm.ScalarMappable(cmap='viridis', norm=plt.Normalize(vmin=min(node_colors), vmax=max(node_colors)))
    sm.set_array([])
    plt.colorbar(sm)
    
    # Show the plot
    plt.show()
    
    dc = pd.DataFrame.from_dict(degree_centrality, orient='index', columns=['degree_centrality'])
    oc = pd.DataFrame.from_dict(out_centrality, orient='index', columns=['out_centrality'])
    ic = pd.DataFrame.from_dict(in_centrality, orient='index', columns=['in_centrality'])
    bc = pd.DataFrame.from_dict(betweenness_centrality, orient='index', columns=['betweenness_centrality'])
    cc = pd.DataFrame.from_dict(closeness_centrality, orient='index', columns=['closeness_centrality'])
    ec = pd.DataFrame.from_dict(eigenvector_centrality, orient='index', columns=['eigenvector_centrality'])
    df = pd.concat([dc,oc,ic,bc,cc,ec], axis=1)
    df.index =TFname
    df = df.sort_values('betweenness_centrality', ascending=False)
    
    return(df)
In [56]:
atri = network(0)
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In [57]:
atri
Out[57]:
degree_centrality out_centrality in_centrality betweenness_centrality closeness_centrality eigenvector_centrality
AT3G19500 1.434057 0.570952 0.863105 0.999079 0.003703 0.108626
AT2G46160 0.886477 0.307179 0.579299 0.997571 0.003446 0.100723
NAI1 0.609349 0.218698 0.390651 0.995092 0.003135 0.086764
BRON 0.686144 0.372287 0.313856 0.993540 0.003159 0.090441
WER 0.794658 0.410684 0.383973 0.991407 0.003341 0.094199
... ... ... ... ... ... ...
HDG1 0.081803 0.061770 0.020033 0.000000 0.001757 0.021656
AT5G23405 0.030050 0.008347 0.021703 0.000000 0.001566 0.009382
EMB2219 0.068447 0.005008 0.063439 0.000000 0.002247 0.018423
SPL2 0.036728 0.010017 0.026711 0.000000 0.002157 0.011940
GL3 0.040067 0.023372 0.016694 0.000000 0.001732 0.012064

600 rows × 6 columns

In [58]:
tri = network(1)
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In [59]:
tri
Out[59]:
degree_centrality out_centrality in_centrality betweenness_centrality closeness_centrality eigenvector_centrality
WRKY72 1.037618 0.557994 0.479624 0.983695 0.003291 0.126875
KDR 1.125392 0.623824 0.501567 0.979151 0.003400 0.127748
GATA2 0.990596 0.695925 0.294671 0.973335 0.003311 0.123841
ATMYC1 0.971787 0.210031 0.761755 0.970505 0.003174 0.130874
RSL1 1.078370 0.592476 0.485893 0.967844 0.003037 0.132763
... ... ... ... ... ... ...
AT5G19490 0.050157 0.025078 0.025078 0.000000 0.001404 0.018192
AT3G07670 0.065831 0.012539 0.053292 0.000000 0.001898 0.022832
AT3G10040 0.062696 0.012539 0.050157 0.000000 0.001792 0.021663
HAT1 0.050157 0.018809 0.031348 0.000000 0.001276 0.017513
AT3G11450 0.056426 0.028213 0.028213 0.000000 0.001464 0.019878

320 rows × 6 columns

In [60]:
cor = network(2)
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In [61]:
cor
Out[61]:
degree_centrality out_centrality in_centrality betweenness_centrality closeness_centrality eigenvector_centrality
TOE3 0.896635 0.182692 0.713942 0.987303 0.008701 0.099010
PLT1 1.021635 0.692308 0.329327 0.985119 0.008012 0.102076
pde191 1.117788 0.509615 0.608173 0.983955 0.008165 0.105965
GATA19 1.396635 0.750000 0.646635 0.983804 0.008642 0.111431
AT3G16280 0.730769 0.086538 0.644231 0.982368 0.008325 0.094588
... ... ... ... ... ... ...
EMB2219 0.014423 0.012019 0.002404 0.000000 0.003297 0.004343
AT5G23405 0.064904 0.031250 0.033654 0.000000 0.004289 0.016626
HDG1 0.072115 0.004808 0.067308 0.000000 0.004927 0.018877
SCL27 0.014423 0.004808 0.009615 0.000000 0.002894 0.003661
AL3 0.007212 0.002404 0.004808 0.000000 0.002918 0.002038

417 rows × 6 columns

In [62]:
end = network(3)
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In [63]:
end
Out[63]:
degree_centrality out_centrality in_centrality betweenness_centrality closeness_centrality eigenvector_centrality
IAA20 0.591797 0.107422 0.484375 0.987949 0.002856 0.092543
WER 0.996094 0.529297 0.466797 0.987479 0.002986 0.109388
CRF3 0.519531 0.175781 0.343750 0.986248 0.002812 0.081648
AGL21 0.478516 0.060547 0.417969 0.977495 0.002872 0.086823
3xHMG-box2 0.542969 0.230469 0.312500 0.976895 0.002326 0.081773
... ... ... ... ... ... ...
AT5G51790 0.023438 0.021484 0.001953 0.000000 0.001540 0.007843
DDL 0.042969 0.017578 0.025391 0.000000 0.001582 0.014723
AT2G36026 0.041016 0.007812 0.033203 0.000000 0.001814 0.014072
BIB 0.212891 0.203125 0.009766 0.000000 0.002112 0.049692
SAP7 0.029297 0.019531 0.009766 0.000000 0.001449 0.009385

513 rows × 6 columns

In [64]:
per = network(4)
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In [65]:
per
Out[65]:
degree_centrality out_centrality in_centrality betweenness_centrality closeness_centrality eigenvector_centrality
LEP 1.511229 0.828269 0.682959 0.994286 0.002940 0.111564
AT3G43430 0.996037 0.550859 0.445178 0.975651 0.002733 0.099988
AT2G34140 0.735799 0.286658 0.449141 0.972119 0.002542 0.087685
AT2G41130 1.129458 0.437252 0.692206 0.969124 0.002782 0.106319
BHLH32 0.780713 0.256275 0.524439 0.965977 0.002719 0.094000
... ... ... ... ... ... ...
AT2G44940 0.096433 0.019815 0.076618 0.000000 0.001735 0.030771
NAC063 0.005284 0.001321 0.003963 0.000000 0.001295 0.001719
bZIP58 0.088507 0.055482 0.033025 0.000000 0.001445 0.027334
ARF9 0.039630 0.003963 0.035667 0.000000 0.001552 0.015674
AL3 0.005284 0.002642 0.002642 0.000000 0.000945 0.001819

758 rows × 6 columns

In [66]:
pro = network(5)
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In [67]:
pro
Out[67]:
degree_centrality out_centrality in_centrality betweenness_centrality closeness_centrality eigenvector_centrality
NST1 0.556622 0.139155 0.417466 0.997523 0.001347 0.077633
OBP2 1.177543 0.459693 0.717850 0.997310 0.001425 0.101482
AT1G61660 1.034549 0.608445 0.426104 0.992280 0.001454 0.094089
AT3G11280 0.786948 0.476967 0.309981 0.992201 0.001358 0.086250
DOF2.4 1.065259 0.306142 0.759117 0.990499 0.001471 0.099403
... ... ... ... ... ... ...
REF6 0.034549 0.011516 0.023033 0.000000 0.000791 0.011252
SNL1 0.092131 0.052783 0.039347 0.000000 0.000857 0.025908
NTM1 0.024952 0.021113 0.003839 0.000000 0.000690 0.009762
HMGB1 0.048944 0.035509 0.013436 0.000000 0.000865 0.015588
AL3 0.015355 0.014395 0.000960 0.000000 0.000668 0.005861

1043 rows × 6 columns

In [68]:
xyl = network(6)
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In [69]:
xyl
Out[69]:
degree_centrality out_centrality in_centrality betweenness_centrality closeness_centrality eigenvector_centrality
TCP15 0.578624 0.308354 0.270270 0.989072 0.001006 0.070293
BHLH32 0.798526 0.465602 0.332924 0.987886 0.001148 0.081793
UNE12 0.697789 0.410319 0.287469 0.985400 0.001118 0.082030
IAA31 0.769042 0.474201 0.294840 0.985352 0.001121 0.080183
MYB12 1.013514 0.416462 0.597052 0.985152 0.001172 0.090284
... ... ... ... ... ... ...
CHB3 0.013514 0.007371 0.006143 0.000000 0.000517 0.004091
CHC1 0.013514 0.004914 0.008600 0.000000 0.000504 0.003885
AT3G08505 0.089681 0.031941 0.057740 0.000000 0.000645 0.021221
CRF8 0.049140 0.033170 0.015971 0.000000 0.000664 0.013663
AT4G22820 0.001229 0.001229 0.000000 0.000000 0.000426 0.000406

815 rows × 6 columns

In [70]:
phl = network(7)
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In [71]:
phl
Out[71]:
degree_centrality out_centrality in_centrality betweenness_centrality closeness_centrality eigenvector_centrality
MYB20 0.895857 0.301232 0.594625 0.995621 0.001181 0.088434
BZIP9 1.108623 0.400896 0.707727 0.993900 0.001340 0.091333
DOF6 0.891377 0.526316 0.365062 0.991496 0.001257 0.088495
AT5G02460 0.982083 0.450168 0.531915 0.990815 0.001279 0.090932
AT1G69580 0.896976 0.431131 0.465845 0.990813 0.001295 0.088092
... ... ... ... ... ... ...
AT3G50650 0.075028 0.038074 0.036954 0.000000 0.000801 0.017592
AT2G24680 0.032475 0.021277 0.011198 0.000000 0.000620 0.008892
B160 0.143337 0.021277 0.122060 0.000000 0.000828 0.035906
bHLH11 0.012318 0.011198 0.001120 0.000000 0.000561 0.004477
AT5G16470 0.053751 0.032475 0.021277 0.000000 0.000690 0.014219

894 rows × 6 columns

In [72]:
lrc = network(8)
No description has been provided for this image
In [73]:
lrc
Out[73]:
degree_centrality out_centrality in_centrality betweenness_centrality closeness_centrality eigenvector_centrality
WER 1.278107 0.535503 0.742604 0.995970 0.011346 0.125005
GATA17 0.757396 0.106509 0.650888 0.994680 0.010797 0.109530
GAMMA-H2AX 0.680473 0.387574 0.292899 0.993460 0.010522 0.101885
HDA3 1.310651 0.547337 0.763314 0.991923 0.011070 0.126188
GATA2 0.538462 0.159763 0.378698 0.987033 0.008242 0.088316
... ... ... ... ... ... ...
NF-YB11 0.065089 0.005917 0.059172 0.000000 0.008549 0.019307
AT5G12980 0.029586 0.008876 0.020710 0.000000 0.004749 0.009932
AT1G68920 0.035503 0.017751 0.017751 0.000000 0.005610 0.012134
AT2G28450 0.023669 0.005917 0.017751 0.000000 0.005751 0.008032
EMB93 0.008876 0.005917 0.002959 0.000000 0.003107 0.003354

339 rows × 6 columns

In [74]:
col = network(9)
No description has been provided for this image
In [75]:
col
Out[75]:
degree_centrality out_centrality in_centrality betweenness_centrality closeness_centrality eigenvector_centrality
RING1 0.907813 0.218750 0.689063 0.995960 0.004748 0.100145
PLT1 1.368750 0.871875 0.496875 0.995738 0.004872 0.111502
HAT1 0.934375 0.364063 0.570312 0.984710 0.004570 0.100171
GATA7 1.342188 0.517188 0.825000 0.984495 0.004814 0.110790
AT1G26680 1.001563 0.414062 0.587500 0.984236 0.003958 0.098139
... ... ... ... ... ... ...
SUVH4 0.071875 0.015625 0.056250 0.000000 0.002556 0.021251
TRFL10 0.015625 0.009375 0.006250 0.000000 0.001577 0.004958
AT1G68070 0.020313 0.009375 0.010938 0.000000 0.001697 0.006449
AT5G61190 0.035938 0.034375 0.001563 0.000000 0.002192 0.012383
MBF1A 0.025000 0.020313 0.004688 0.000000 0.002029 0.008504

641 rows × 6 columns

In [76]:
atri.columns = ['atri_degree_centrality','atri_out_centrality','atri_in_centrality','atri_betweenness_centrality','atri_closeness_centrality','atri_eigenvector_centrality']
tri.columns = ['tri_degree_centrality','tri_out_centrality','tri_in_centrality','tri_betweenness_centrality','tri_closeness_centrality','tri_eigenvector_centrality']
cor.columns = ['cor_degree_centrality','cor_out_centrality','cor_in_centrality','cor_betweenness_centrality','cor_closeness_centrality','cor_eigenvector_centrality']
end.columns = ['end_degree_centrality','end_out_centrality','end_in_centrality','end_betweenness_centrality','end_closeness_centrality','end_eigenvector_centrality']
per.columns = ['per_degree_centrality','per_out_centrality','per_in_centrality','per_betweenness_centrality','per_closeness_centrality','per_eigenvector_centrality']
pro.columns = ['pro_degree_centrality','pro_out_centrality','pro_in_centrality','pro_betweenness_centrality','pro_closeness_centrality','pro_eigenvector_centrality']
xyl.columns = ['xyl_degree_centrality','xyl_out_centrality','xyl_in_centrality','xyl_betweenness_centrality','xyl_closeness_centrality','xyl_eigenvector_centrality']
phl.columns = ['phl_degree_centrality','phl_out_centrality','phl_in_centrality','phl_betweenness_centrality','phl_closeness_centrality','phl_eigenvector_centrality']
lrc.columns = ['lrc_degree_centrality','lrc_out_centrality','lrc_in_centrality','lrc_betweenness_centrality','lrc_closeness_centrality','lrc_eigenvector_centrality']
col.columns = ['col_degree_centrality','col_out_centrality','col_in_centrality','col_betweenness_centrality','col_closeness_centrality','col_eigenvector_centrality']
In [77]:
## Indentify main regulators in each net work
tff = []
tff = tff + atri[atri['atri_betweenness_centrality']>0].index.tolist()
tff = tff + tri[tri['tri_betweenness_centrality']>0].index.tolist()
tff = tff + lrc[lrc['lrc_betweenness_centrality']>0].index.tolist()
tff = tff + cor[cor['cor_betweenness_centrality']>0].index.tolist()
tff = tff + end[end['end_betweenness_centrality']>0].index.tolist()
tff = tff + per[per['per_betweenness_centrality']>0].index.tolist()
tff = tff + pro[pro['pro_betweenness_centrality']>0].index.tolist()
tff = tff + xyl[xyl['xyl_betweenness_centrality']>0].index.tolist()
tff = tff + phl[phl['phl_betweenness_centrality']>0].index.tolist()
tff = tff + col[col['col_betweenness_centrality']>0].index.tolist()
tf_occurance = pd.DataFrame(pd.Series(tff).value_counts())
tf_occurance = tf_occurance.rename(columns={
    'count': 'tf_occurance'
})
tf_spec = pd.concat([tf_occurance, atri, tri, lrc, cor, end, per, pro, xyl, phl, col], axis=1)
tf_spec = tf_spec.fillna(0)
In [78]:
## Epidermis (atri, tri, lrc)
celltype1='atri'
celltype2='tri'
celltype3='lrc'
ts = tf_spec[tf_spec['tf_occurance']==3][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype3+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype3+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality', celltype3+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==9].sort_values(['centrality_count','centrality_sum'], ascending=False)
Out[78]:
atri_betweenness_centrality tri_betweenness_centrality lrc_betweenness_centrality atri_out_centrality tri_out_centrality lrc_out_centrality atri_in_centrality tri_in_centrality lrc_in_centrality centrality_count centrality_sum
In [79]:
## atri, tri
celltype1='atri'
celltype2='tri'
ts = tf_spec[tf_spec['tf_occurance']==2][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==6].sort_values(['centrality_count','centrality_sum'], ascending=False)
Out[79]:
atri_betweenness_centrality tri_betweenness_centrality atri_out_centrality tri_out_centrality atri_in_centrality tri_in_centrality centrality_count centrality_sum
WRKY72 0.067258 0.983695 0.168614 0.557994 0.025042 0.479624 6 8.282227
MYB23 0.336575 0.410865 0.338898 0.244514 0.098497 0.134796 6 7.564147
AT2G36026 0.000293 0.614016 0.116861 0.260188 0.026711 0.128527 6 7.146596
AT1G61990 0.668014 0.000138 0.053422 0.106583 0.080134 0.112853 6 7.021144
RHD6 0.007680 0.574693 0.086811 0.178683 0.035058 0.094044 6 6.976970
WHY3 0.000003 0.030697 0.108514 0.297806 0.031720 0.137931 6 6.606671
WRKY27 0.000779 0.004022 0.116861 0.137931 0.028381 0.072100 6 6.360074
LOL2 0.000073 0.000611 0.053422 0.087774 0.006678 0.028213 6 6.176771
In [80]:
## Atrichoblast specific
celltype = 'atri'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[80]:
atri_betweenness_centrality atri_out_centrality atri_in_centrality centrality_count centrality_sum
DAR4 0.932745 0.051753 0.183639 3 4.168137
TTG2 0.751967 0.198664 0.118531 3 4.069162
DDL 0.447426 0.043406 0.071786 3 3.562618
PIE1 0.397563 0.028381 0.051753 3 3.477697
MBD6 0.390082 0.058431 0.070117 3 3.518629
AT3G11450 0.060002 0.121870 0.050083 3 3.231956
AT2G23060 0.045354 0.010017 0.095159 3 3.150530
AT1G19860 0.014648 0.008347 0.043406 3 3.066401
AT5G07400 0.009913 0.028381 0.053422 3 3.091716
AT4G14490 0.004403 0.153589 0.026711 3 3.184703
AT1G76350 0.003579 0.033389 0.078464 3 3.115432
SUVH1 0.003356 0.023372 0.096828 3 3.123556
TIFY8 0.002233 0.013356 0.071786 3 3.087375
MBD13 0.001823 0.030050 0.033389 3 3.065262
AT1G62110 0.001058 0.013356 0.020033 3 3.034447
MBD10 0.000209 0.043406 0.218698 3 3.262313
FIT 0.000006 0.115192 0.016694 3 3.131892
REF6 0.000006 0.076795 0.068447 3 3.145248
AT2G36340 0.000003 0.040067 0.011686 3 3.051756
In [81]:
## Trichoblast specific
celltype = 'tri'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[81]:
tri_betweenness_centrality tri_out_centrality tri_in_centrality centrality_count centrality_sum
OFP13 0.747264 0.357367 0.094044 3 4.198675
TCP24 0.676781 0.068966 0.184953 3 3.930699
AGL87 0.665296 0.109718 0.181818 3 3.956832
WRKY9 0.548777 0.260188 0.078370 3 3.887335
AT3G53370 0.503559 0.620690 0.084639 3 4.208888
MYB47 0.079395 0.288401 0.090909 3 3.458705
AT5G64950 0.069458 0.050157 0.047022 3 3.166637
HDG1 0.049191 0.053292 0.109718 3 3.212200
IAA14 0.044045 0.294671 0.056426 3 3.395142
AT3G24490 0.017202 0.043887 0.084639 3 3.145729
ERF104 0.013200 0.094044 0.065831 3 3.173074
AT1G11490 0.009493 0.097179 0.078370 3 3.185042
AT1G61960 0.009020 0.050157 0.087774 3 3.146951
AT3G09735 0.006477 0.184953 0.043887 3 3.235317
WRKY33 0.004505 0.018809 0.178683 3 3.201997
RAD54 0.001429 0.040752 0.269592 3 3.311774
ESE3 0.000020 0.495298 0.015674 3 3.510992
In [82]:
## LRC specific
celltype = 'lrc'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[82]:
lrc_betweenness_centrality lrc_out_centrality lrc_in_centrality centrality_count centrality_sum
In [83]:
## Columella specific
celltype = 'col'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[83]:
col_betweenness_centrality col_out_centrality col_in_centrality centrality_count centrality_sum
AT3G19184 0.951467 0.060938 0.321875 3 4.334280
NAC016 0.948863 0.306250 0.307813 3 4.562925
HSF A4A 0.811752 0.053125 0.109375 3 3.974252
AGL94 0.628365 0.110938 0.104688 3 3.843990
AT2G32030 0.547386 0.017188 0.118750 3 3.683324
AXR3 0.294195 0.340625 0.040625 3 3.675445
AT5G09240 0.282583 0.114063 0.125000 3 3.521645
B160 0.206675 0.079688 0.068750 3 3.355113
AT3G52440 0.192383 0.365625 0.026563 3 3.584571
AGL102 0.054915 0.054688 0.107813 3 3.217415
HSFC1 0.029106 0.075000 0.195312 3 3.299418
MYB95 0.020721 0.134375 0.012500 3 3.167596
AT1G05920 0.020119 0.035938 0.046875 3 3.102932
NAC052 0.009588 0.203125 0.029688 3 3.242400
ARF10 0.005233 0.121875 0.045313 3 3.172420
COL5 0.000538 0.042188 0.132812 3 3.175538
TRFL1 0.000017 0.090625 0.067187 3 3.157830
AT1G68920 0.000010 0.089063 0.009375 3 3.098447
In [84]:
## Ground tissue
celltype1='cor'
celltype2='end'
ts = tf_spec[tf_spec['tf_occurance']==2][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==6].sort_values(['centrality_count','centrality_sum'], ascending=False)
Out[84]:
cor_betweenness_centrality end_betweenness_centrality cor_out_centrality end_out_centrality cor_in_centrality end_in_centrality centrality_count centrality_sum
JKD 0.161394 0.499438 0.350962 0.304688 0.009615 0.064453 6 7.390549
SIGF 0.315506 0.035840 0.048077 0.017578 0.098558 0.076172 6 6.591731
AT4G23860 0.242470 0.000260 0.062500 0.017578 0.050481 0.035156 6 6.408445
AT1G03650 0.004657 0.003027 0.060096 0.046875 0.052885 0.160156 6 6.327696
TSO1 0.000006 0.002997 0.012019 0.007812 0.016827 0.052734 6 6.092395
In [85]:
## Cortex specific
celltype = 'cor'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[85]:
cor_betweenness_centrality cor_out_centrality cor_in_centrality centrality_count centrality_sum
HB4 0.127016 0.153846 0.007212 3 3.288073
AT1G60700 0.015234 0.014423 0.112981 3 3.142638
ELO3 0.000122 0.103365 0.016827 3 3.120314
In [86]:
## Endodermis specific
celltype = 'end'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[86]:
end_betweenness_centrality end_out_centrality end_in_centrality centrality_count centrality_sum
MYB68 0.539406 0.562500 0.314453 3 4.416360
MYB36 0.500134 0.693359 0.359375 3 4.552868
MYB3 0.455468 0.640625 0.146484 3 4.242577
SCR 0.287797 0.552734 0.220703 3 4.061235
RVN 0.093742 0.248047 0.070312 3 3.412102
AT3G13040 0.089817 0.021484 0.070312 3 3.181614
BSM 0.054638 0.015625 0.052734 3 3.122997
HAT7 0.023720 0.576172 0.015625 3 3.615517
AT1G72210 0.014341 0.179688 0.029297 3 3.223325
JMJD5 0.011180 0.017578 0.029297 3 3.058055
ERF15 0.008676 0.148438 0.027344 3 3.184458
AL1 0.003329 0.033203 0.076172 3 3.112704
SCL3 0.003234 0.126953 0.076172 3 3.206359
AT4G00390 0.002439 0.031250 0.015625 3 3.049314
VRN1 0.002381 0.074219 0.005859 3 3.082459
APRR2 0.002018 0.042969 0.003906 3 3.048893
AT5G13780 0.000019 0.046875 0.093750 3 3.140644
In [87]:
## Stele
celltype1='per'
celltype2='pro'
celltype3='xyl'
celltype4='phl'
ts = tf_spec[tf_spec['tf_occurance']==4][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype3+'_betweenness_centrality', celltype4+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype3+'_out_centrality', celltype4+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality', celltype3+'_in_centrality', celltype4+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==12].sort_values(['centrality_count','centrality_sum'], ascending=False)
Out[87]:
per_betweenness_centrality pro_betweenness_centrality xyl_betweenness_centrality phl_betweenness_centrality per_out_centrality pro_out_centrality xyl_out_centrality phl_out_centrality per_in_centrality pro_in_centrality xyl_in_centrality phl_in_centrality centrality_count centrality_sum
AT3G43430 0.975651 0.730029 0.939741 0.775860 0.550859 0.619962 0.248157 0.679731 0.445178 0.282150 0.314496 0.349384 12 18.911198
AT1G69580 0.761739 0.983678 0.920997 0.990813 0.095112 0.287908 0.085995 0.431131 0.169089 0.656430 0.304668 0.465845 12 18.153405
DAG1 0.870339 0.650648 0.727170 0.853704 0.330251 0.393474 0.088452 0.510638 0.286658 0.342610 0.237101 0.301232 12 17.592277
AT4G30410 0.930193 0.875508 0.950479 0.540403 0.466314 0.217850 0.213759 0.347144 0.282695 0.171785 0.337838 0.070549 12 17.404518
IAA13 0.841542 0.962271 0.758644 0.964890 0.141347 0.234165 0.090909 0.115342 0.182299 0.194818 0.178133 0.258679 12 16.923038
BT1 0.247636 0.955381 0.891375 0.608766 0.142668 0.276392 0.357494 0.300112 0.183620 0.326296 0.388206 0.190370 12 16.868315
AT3G60490 0.605146 0.559458 0.000029 0.962918 0.244386 0.439539 0.011057 0.639418 0.116248 0.564299 0.081081 0.625980 12 16.849558
DOF2.4 0.000619 0.990499 0.464114 0.931302 0.031704 0.306142 0.082310 0.646137 0.046235 0.759117 0.105651 0.444569 12 16.808397
ABO3 0.694575 0.914774 0.002137 0.754951 0.100396 0.634357 0.199017 0.558791 0.121532 0.334933 0.045455 0.389698 12 16.750616
UNE12 0.572418 0.543240 0.985400 0.756611 0.120211 0.379079 0.410319 0.281075 0.113606 0.114203 0.287469 0.080627 12 16.644259
IAA11 0.890603 0.941530 0.529070 0.929096 0.097754 0.166027 0.212531 0.157895 0.203435 0.161228 0.060197 0.203807 12 16.553172
IAA12 0.020685 0.877714 0.948912 0.979738 0.044914 0.112284 0.457002 0.440090 0.034346 0.170825 0.218673 0.236282 12 16.541467
LBD38 0.288952 0.874569 0.308618 0.981562 0.103038 0.482726 0.120393 0.264278 0.089828 0.368522 0.088452 0.483763 12 16.454701
HB-7 0.000229 0.928357 0.479410 0.876094 0.091149 0.481766 0.119165 0.322508 0.040951 0.307102 0.142506 0.655095 12 16.444332
bZIP4 0.798582 0.983328 0.683278 0.547665 0.138705 0.122841 0.184275 0.051512 0.158520 0.338772 0.101966 0.106383 12 16.215826
HB40 0.000536 0.871393 0.594975 0.876708 0.134742 0.150672 0.420147 0.353863 0.187583 0.132438 0.170762 0.316909 12 16.210729
AT5G51780 0.857380 0.949088 0.847670 0.003176 0.339498 0.149712 0.192875 0.215006 0.202114 0.133397 0.151106 0.006719 12 16.047741
RVE2 0.786237 0.838376 0.097467 0.954120 0.143989 0.121881 0.101966 0.329227 0.128137 0.177543 0.079853 0.241881 12 16.000678
IAA26 0.000003 0.839292 0.835207 0.667949 0.047556 0.134357 0.234644 0.333707 0.070013 0.213052 0.275184 0.150056 12 15.801020
AT3G06590 0.735944 0.811649 0.000015 0.737821 0.363276 0.118042 0.065111 0.254199 0.103038 0.171785 0.044226 0.073908 12 15.479015
NAC001 0.172021 0.808227 0.180414 0.793703 0.140026 0.320537 0.152334 0.182531 0.087186 0.315739 0.079853 0.191489 12 15.424061
AT5G50010 0.864115 0.192200 0.756959 0.417591 0.215324 0.106526 0.179361 0.287794 0.142668 0.016315 0.142506 0.048152 12 15.369512
RMA2 0.035893 0.933214 0.001180 0.946316 0.017173 0.233205 0.159705 0.309071 0.091149 0.295585 0.009828 0.270997 12 15.303317
AT4G24060 0.018471 0.808452 0.128876 0.411065 0.140026 0.322457 0.168305 0.212766 0.203435 0.319578 0.187961 0.359462 12 15.280854
WOX13 0.744622 0.000548 0.285313 0.783526 0.145310 0.091171 0.452088 0.090705 0.190225 0.124760 0.031941 0.126540 12 15.066748
ERF12 0.043644 0.701782 0.164228 0.786206 0.092470 0.326296 0.192875 0.231803 0.054161 0.208253 0.062654 0.201568 12 15.065938
AT5G07580 0.000211 0.356549 0.555603 0.148994 0.095112 0.570058 0.250614 0.387458 0.022457 0.223608 0.097052 0.169093 12 14.876810
MYBR1 0.000349 0.227427 0.515395 0.959420 0.027741 0.131478 0.103194 0.123180 0.080581 0.080614 0.108108 0.137738 12 14.495226
AT2G29660 0.004797 0.025269 0.816665 0.081259 0.062087 0.085413 0.212531 0.296753 0.021136 0.019194 0.227273 0.055991 12 13.908366
MYB32 0.000005 0.000213 0.723294 0.002333 0.104359 0.048944 0.233415 0.195969 0.029062 0.007678 0.186732 0.011198 12 13.543202
SPL2 0.000902 0.238445 0.000612 0.445605 0.039630 0.032630 0.109337 0.049272 0.052840 0.143954 0.036855 0.115342 12 13.265423
AT5G09460 0.000729 0.001225 0.420722 0.082343 0.029062 0.052783 0.183047 0.147816 0.056803 0.025912 0.031941 0.067189 12 13.099572
In [88]:
## Pericycle
celltype = 'per'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[88]:
per_betweenness_centrality per_out_centrality per_in_centrality centrality_count centrality_sum
AT3G61550 0.799494 0.076618 0.097754 3 3.973866
AT4G20970 0.713314 0.091149 0.116248 3 3.920712
ERF10 0.521286 0.187583 0.151915 3 3.860784
GATA23 0.450134 0.170410 0.076618 3 3.697162
IDD11 0.264213 0.216645 0.130779 3 3.611637
TRFL7 0.029448 0.034346 0.026420 3 3.090214
DBP1 0.024425 0.019815 0.025099 3 3.069339
AT1G78930 0.012179 0.018494 0.071334 3 3.102007
ASHH3 0.001106 0.022457 0.047556 3 3.071119
RTV1 0.001015 0.043593 0.038309 3 3.082917
AT1G62120 0.000557 0.043593 0.030383 3 3.074534
CSDP1 0.000404 0.026420 0.075297 3 3.102121
BIM1 0.000231 0.054161 0.056803 3 3.111195
AT1G03150 0.000197 0.029062 0.027741 3 3.057001
AT5G44080 0.000135 0.019815 0.036988 3 3.056938
AT5G12440 0.000010 0.035667 0.021136 3 3.056814
E2F3 0.000007 0.029062 0.026420 3 3.055489
In [89]:
## Procambium
celltype = 'pro'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[89]:
pro_betweenness_centrality pro_out_centrality pro_in_centrality centrality_count centrality_sum
AT1G01640 5.405486e-01 0.056622 0.021113 3 3.618284
AT1G62975 4.369792e-01 0.081574 0.030710 3 3.549263
bZIP23 5.652877e-02 0.012476 0.003839 3 3.072844
HB-1 4.612703e-02 0.014395 0.047985 3 3.108507
JAZ6 3.604518e-02 0.055662 0.048944 3 3.140652
BEH2 3.033035e-03 0.043186 0.119002 3 3.165221
CDF2 2.974034e-03 0.064299 0.083493 3 3.150767
KNAT6 2.502024e-03 0.095969 0.054702 3 3.153174
LBD29 1.562612e-03 0.136276 0.068138 3 3.205977
OBF5 1.273137e-03 0.090211 0.038388 3 3.129872
PTF1 6.517799e-04 0.038388 0.031670 3 3.070709
WRKY2 4.351345e-04 0.038388 0.060461 3 3.099284
STOP1 2.774905e-04 0.074856 0.058541 3 3.133675
NTL9 1.355186e-04 0.109405 0.154511 3 3.264051
EICBP.B 1.180026e-04 0.027831 0.079655 3 3.107604
MYB33 4.793855e-05 0.041267 0.070058 3 3.111372
AT1G05120 3.964149e-05 0.100768 0.066219 3 3.167026
HK3 2.120359e-05 0.056622 0.071977 3 3.128620
NF-YC9 5.531371e-06 0.017274 0.039347 3 3.056627
AL4 9.218952e-07 0.070058 0.059501 3 3.129559
In [90]:
## Xylem
celltype = 'xyl'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[90]:
xyl_betweenness_centrality xyl_out_centrality xyl_in_centrality centrality_count centrality_sum
VND4 0.983333 0.386978 0.380835 3 4.751146
MYB46 0.977289 0.533170 0.361179 3 4.871637
VND7 0.955970 0.420147 0.377150 3 4.753268
MYB50 0.942537 0.119165 0.402948 3 4.464650
VND1 0.941278 0.342752 0.326781 3 4.610811
VND5 0.921148 0.374693 0.297297 3 4.593138
AT1G68200 0.908428 0.147420 0.171990 3 4.227838
BBX31 0.887668 0.355037 0.181818 3 4.424523
TCP10 0.886201 0.149877 0.281327 3 4.317405
HB31 0.884470 0.305897 0.380835 3 4.571202
MYB99 0.869882 0.130221 0.179361 3 4.179464
AGL58 0.857817 0.238329 0.157248 3 4.253395
AT1G26610 0.839940 0.092138 0.173219 3 4.105296
VND6 0.831321 0.195332 0.120393 3 4.147045
MYB83 0.803807 0.186732 0.163391 3 4.153930
MYB63 0.798121 0.146192 0.108108 3 4.052421
AT5G03510 0.778459 0.513514 0.271499 3 4.563471
AT5G18090 0.684339 0.153563 0.202703 3 4.040604
BZIP49 0.623033 0.173219 0.171990 3 3.968242
LBD18 0.370427 0.513514 0.036855 3 3.920796
LAS 0.175655 0.027027 0.138821 3 3.341502
SHP1 0.136986 0.114251 0.054054 3 3.305291
HB34 0.108732 0.116708 0.078624 3 3.304064
BEE2 0.065623 0.071253 0.085995 3 3.222871
JAZ12 0.065355 0.025799 0.081081 3 3.172235
BZIP24 0.044568 0.088452 0.131450 3 3.264469
AGL64 0.022811 0.018428 0.073710 3 3.114949
AT5G14370 0.018089 0.067568 0.034398 3 3.120055
AT5G06770 0.016064 0.128993 0.040541 3 3.185597
AT4G14920 0.009996 0.012285 0.063882 3 3.086163
AT3G45880 0.009786 0.052826 0.073710 3 3.136321
RAP2.1 0.009751 0.366093 0.022113 3 3.397957
ZHD3 0.008764 0.300983 0.013514 3 3.323261
ATXR5 0.005393 0.065111 0.095823 3 3.166327
NAC003 0.003686 0.237101 0.006143 3 3.246929
SDG16 0.002720 0.018428 0.131450 3 3.152597
BLH3 0.002026 0.171990 0.003686 3 3.177702
AL5 0.002014 0.023342 0.098280 3 3.123636
LBD31 0.001908 0.425061 0.024570 3 3.451540
FRS10 0.001635 0.025799 0.027027 3 3.054461
BZIP17 0.001473 0.030713 0.024570 3 3.056756
HB30 0.000786 0.099509 0.030713 3 3.131007
BBX28 0.000536 0.051597 0.072482 3 3.124615
VFP5 0.000500 0.012285 0.036855 3 3.049640
AT3G16350 0.000227 0.083538 0.027027 3 3.110792
RDUF1 0.000118 0.015971 0.031941 3 3.048029
AT1G29560 0.000048 0.050369 0.070025 3 3.120441
MYC4 0.000003 0.004914 0.036855 3 3.041772
In [91]:
## Phloem
celltype = 'phl'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[91]:
phl_betweenness_centrality phl_out_centrality phl_in_centrality centrality_count centrality_sum
AT3G46600 0.948114 0.095185 0.170213 3 4.213512
SPL10 0.930477 0.061590 0.198208 3 4.190275
AT2G03500 0.708464 0.481523 0.278835 3 4.468822
AT3G12730 0.602997 0.395297 0.211646 3 4.209940
AT4G39780 0.541354 0.098544 0.095185 3 3.735083
RAP2.7 0.524287 0.117581 0.104143 3 3.746012
MYB10 0.438067 0.297872 0.076148 3 3.812087
MYB55 0.296968 0.043673 0.221725 3 3.562366
WRKY45 0.194015 0.144457 0.033595 3 3.372067
SIGC 0.177906 0.021277 0.071669 3 3.270851
WLIM1 0.086205 0.122060 0.027996 3 3.236261
GT2 0.084196 0.058231 0.060470 3 3.202897
AT1G32360 0.067105 0.021277 0.035834 3 3.124216
AT5G41380 0.051674 0.166853 0.027996 3 3.246523
AT3G02890 0.050278 0.026876 0.079507 3 3.156661
HMGB1 0.026843 0.035834 0.033595 3 3.096272
AT1G75510 0.022340 0.025756 0.063830 3 3.111926
AT1G69170 0.020631 0.049272 0.097424 3 3.167328
DA1 0.015449 0.022396 0.033595 3 3.071440
NAC086 0.011925 0.071669 0.064950 3 3.148543
AT3G42860 0.010872 0.031355 0.118701 3 3.160928
NF-YB12 0.008665 0.021277 0.055991 3 3.085932
AT5G64220 0.005013 0.013438 0.075028 3 3.093479
AIF1 0.004953 0.181411 0.085106 3 3.271470
AT5G12400 0.004159 0.022396 0.064950 3 3.091505
KNAT2 0.003642 0.135498 0.051512 3 3.190652
AT4G22820 0.003033 0.008959 0.021277 3 3.033268
ZAP1 0.002758 0.040314 0.020157 3 3.063228
AT1G02030 0.001864 0.025756 0.044793 3 3.072413
EMB1674 0.001765 0.036954 0.139978 3 3.178697
SPL11 0.001225 0.025756 0.083987 3 3.110968
LUG 0.001208 0.015677 0.033595 3 3.050480
AT5G08630 0.001066 0.010078 0.029115 3 3.040260
EDA16 0.000916 0.012318 0.163494 3 3.176728
E2F1 0.000619 0.029115 0.059351 3 3.089085
SIZ1 0.000551 0.019037 0.051512 3 3.071100
TBP2 0.000491 0.026876 0.040314 3 3.067680
AT3G13350 0.000412 0.049272 0.027996 3 3.077679
GBF2 0.000188 0.036954 0.045913 3 3.083055
HB5 0.000156 0.039194 0.105263 3 3.144613
HAT3.1 0.000068 0.023516 0.034714 3 3.058298
BPC1 0.000045 0.054871 0.063830 3 3.118746
HAM2 0.000015 0.045913 0.057111 3 3.103039
AT2G27580 0.000015 0.038074 0.040314 3 3.078403
ALY2 0.000009 0.031355 0.006719 3 3.038083
NF-YC3 0.000003 0.049272 0.055991 3 3.105266
CRF8 0.000001 0.040314 0.033595 3 3.073909
TRFL5 0.000001 0.007839 0.039194 3 3.047034
GATA1 0.000001 0.033595 0.125420 3 3.159016

Search for individual genes¶

In [92]:
gene = 'SHR'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]
Out[92]:
tf_occurance end_degree_centrality end_out_centrality end_in_centrality end_betweenness_centrality end_closeness_centrality end_eigenvector_centrality per_degree_centrality per_out_centrality per_in_centrality ... xyl_out_centrality xyl_in_centrality xyl_betweenness_centrality xyl_closeness_centrality xyl_eigenvector_centrality phl_degree_centrality phl_out_centrality phl_in_centrality phl_closeness_centrality phl_eigenvector_centrality
SHR 4.0 0.162109 0.060547 0.101562 0.838689 0.001708 0.04138 0.612946 0.389696 0.22325 ... 0.30344 0.302211 0.930201 0.001047 0.077013 0.051512 0.005599 0.045913 0.000836 0.015495

1 rows × 30 columns

In [93]:
gene = 'BLJ'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]
Out[93]:
end_degree_centrality end_out_centrality end_in_centrality end_closeness_centrality end_eigenvector_centrality
BLJ 0.28125 0.271484 0.009766 0.002145 0.060075
In [94]:
gene = 'JKD'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]
Out[94]:
tf_occurance atri_degree_centrality atri_out_centrality atri_in_centrality atri_closeness_centrality atri_eigenvector_centrality cor_degree_centrality cor_out_centrality cor_in_centrality cor_betweenness_centrality ... end_out_centrality end_in_centrality end_betweenness_centrality end_closeness_centrality end_eigenvector_centrality col_degree_centrality col_out_centrality col_in_centrality col_closeness_centrality col_eigenvector_centrality
JKD 2.0 0.016694 0.003339 0.013356 0.001438 0.005749 0.360577 0.350962 0.009615 0.161394 ... 0.304688 0.064453 0.499438 0.002302 0.073275 0.007812 0.003125 0.004688 0.001647 0.003063

1 rows × 23 columns

In [95]:
gene = 'RVN'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]
Out[95]:
tf_occurance end_degree_centrality end_out_centrality end_in_centrality end_betweenness_centrality end_closeness_centrality end_eigenvector_centrality
RVN 1.0 0.318359 0.248047 0.070312 0.093742 0.002287 0.064905
In [96]:
gene = 'BIB'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]
Out[96]:
end_degree_centrality end_out_centrality end_in_centrality end_closeness_centrality end_eigenvector_centrality
BIB 0.212891 0.203125 0.009766 0.002112 0.049692
In [97]:
gene = 'IME'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]
Out[97]:
In [98]:
gene = 'MYB66'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]
Out[98]:
In [99]:
gene = 'GL2'
tf_spec[tf_spec.index==gene][tf_spec[tf_spec.index==gene].columns[tf_spec[tf_spec.index==gene].any()]]
Out[99]:
tf_occurance atri_degree_centrality atri_out_centrality atri_in_centrality atri_betweenness_centrality atri_closeness_centrality atri_eigenvector_centrality tri_degree_centrality tri_out_centrality tri_in_centrality ... cor_out_centrality cor_in_centrality cor_betweenness_centrality cor_closeness_centrality cor_eigenvector_centrality end_degree_centrality end_out_centrality end_in_centrality end_closeness_centrality end_eigenvector_centrality
GL2 2.0 0.519199 0.380634 0.138564 0.849063 0.002686 0.075841 0.128527 0.050157 0.07837 ... 0.165865 0.360577 0.917395 0.00696 0.081382 0.058594 0.037109 0.021484 0.001511 0.017833

1 rows × 28 columns

In [101]:
tf_spec.to_csv('TF_GRN_centrality_t0-t1_zscore3.csv', index=True)
In [ ]: